PhD in Computer Architecture for Real-Time AI at the Edge

16 Jan 2026
Job Information

Organisation/Company

Eindhoven University of Technology (TU/e)

Research Field

Computer science » Computer architecture
Computer science » Computer hardware

Researcher Profile

First Stage Researcher (R1)

Application Deadline

14 Feb 2026 - 22:59 (UTC)

Country

Netherlands

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

36.0

Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Join the NWO Perspectief FIND program and help design next-generation computer architectures for running large AI models on embedded and edge systems under strict timing, energy, and memory constraints. You’ll explore hardware-aware optimization and co-design with accelerators (FPGAs, GPUs, near-memory systems) to achieve real-time, energy-efficient AI for high-tech industry applications. Work with leading companies like ASMPT and shape the future of AI at the edge!
Information
Industrial edge deployments—in semiconductor manufacturing, printing systems, automotive radar, smart mobility cameras, and HealthTech—require on-device AI to ensure low latency, privacy, and resilience. Today’s Transformers models scale poorly and assume abundant cloud resources. The research program FIND aims to deliver architectural and algorithmic breakthroughs that enable foundation models to run predictably and efficiently on embedded processors and accelerators.
FIND is a research program funded by the Dutch government and industry that brings together 5 universities, 11 companies (startups to multinationals), and 2 knowledge institutes to develop foundation models (large AI models) for Dutch high‑tech industry, with strong emphasis on edge deployment, privacy, and timely decision‑making. Partners include ASML, NXP, Canon, ASMPT, Technolution, Signify, Shell, Stryker, TNO, and others. A total of 12 PhDs will be employed on the FIND program covering topics from foundation model pre-training and multimodal adaptation to architectures and compression for edge deployment while targeting real-world validation in domains like HealthTech, smart industry, and autonomous mobility.
This PhD position focuses on the design of novel computer architectures to enable large AI models to run on embedded and edge systems under strict timing, energy, and memory constraints. Current solutions typically rely on general-purpose CPUs or GPUs, which are optimized for flexibility rather than predictable real-time performance. These platforms struggle with the complexity and resource demands of foundation models, leading to high latency, excessive energy consumption, and large memory footprints—making them unsuitable for safety-critical or resource-constrained environments.
In addition, you will explore model compression and co-optimization with hardware accelerators. Unlike existing approaches that apply compression techniques (e.g., pruning, quantization) in isolation, this research will jointly optimize algorithms and hardware. Co-optimization ensures that architectural decisions—such as token merging, sparsity exploitation, and quantization—are aligned with hardware datapaths and memory hierarchies. This enables predictable real-time performance, energy efficiency, and scalability across heterogeneous platforms (embedded CPUs, GPUs, FPGAs, near-memory accelerators). The result is a shift from “best-effort” execution on generic hardware to deterministic, deadline-compliant AI inference tailored for industrial edge systems.
These architectures and accelerators will be prototyped and validated in collaboration with a leading high-tech semiconductor manufacturing company, demonstrating how edge devices can execute foundation models with guaranteed performance and minimal energy footprint—closing the gap between AI capability and embedded system constraints.
Research group and company
This position is embedded in the Electronic Systems (ES) group within the Electrical Engineering department at Eindhoven University of Technology (TU/e). Our group consists of six full professors, three associate professors, seven assistant professors, several postdocs, approximately 40 EngD and PhD candidates, and support staff. The ES group is world-renowned for its design automation and embedded systems research.
This PhD project is executed in close collaboration with ASMPT which is the only company in the world offering a comprehensive portfolio of integrated solutions for all major steps in electronics manufacturing – from carrier for chip interconnection to chip assembly and packaging to SMT.

Where to apply

Website

https://www.academictransfer.com/en/jobs/357773/phd-in-computer-architecture-fo…

Requirements
Specific Requirements

• A master’s degree (or an equivalent university degree) in Computer Engineering, Electrical Engineering, Embedded Systems, or related background and strong hardware design skills.
• Strong background in computer architecture, digital design, and real-time systems.
• Knowledge of embedded systems and hardware/software co-design.
• Experience with hardware acceleration (FPGAs, GPUs, SoCs) and low-power design.
• Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow) is a plus.
• Ability to work in an interdisciplinary team.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

Additional Information
Benefits

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,059 - max. € 3,881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process .
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

Selection process

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.

Ensure that you submit all the requested application documents. We give priority to complete applications.
We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Additional comments

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager dr.ir. Sander Stuijk, s.stuijk@tue.nl .
Visit our website for more information about the application process or the conditions of employment . You can also contact Kevin Caris, HR advisor, k.t.caris@tue.nl or +31 40 247 8835.
Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video .
Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Website for additional job details

https://www.academictransfer.com/357773/

Work Location(s)

Number of offers available

1

Company/Institute

TU/e

Country

Netherlands

City

Eindhoven

Postal Code

5612AZ

Street

De Zaale

Geofield

Contact

City

Eindhoven

Website

http://www.tue.nl/

Street

De Rondom 70

Postal Code

5612 AP

STATUS: EXPIRED

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